Information processing method and apparatus

ABSTRACT

An information processing method and apparatus, the method including: training a Deep Neural Network (DNN) by using an evaluation object seed, an evaluation term seed and an evaluation relationship seed (101); at a first input layer, connecting vectors corresponding to a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship to obtain a first input vector (102); at a first hidden layer, compressing the first input vector to obtain a first middle vector, and at a first output layer, decoding the first middle vector to obtain a first output vector (103); and determining a first output vector whose decoding error value is less than a decoding error value threshold, and determining a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship corresponding to the determined first output vector as first opinion information (104). By use of the technical solution, precision of extracting opinion information from an evaluation text can be enhanced.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/CN2015/081274, filed on Jun. 11, 2015. This application claims thebenefit and priority of Chinese Application No. 201410271542.2, filed onJun. 18, 2014. The entire disclosures of each of the above applicationsare incorporated herein by reference.

FIELD OF THE TECHNOLOGY

The present disclosure relates to opinion mining technologies and to aninformation processing method and apparatus in deep neural network.

BACKGROUND OF THE DISCLOSURE

With rapid expansion of the Internet, online shopping has becomeincreasingly popular and many online shopping websites provide a productevaluation platform, making it convenient for users to share product useexperience and to make comments about products. The comments are ofimportant reference value for both consumers and product providers.

Currently, the related art uses an opinion mining (also known as commentmining) apparatus to analyze an evaluation text (also known as corpus)from the product evaluation platform, to obtain users' opinioninformation of the products. However, tests have proven that theprecision rate of opinion information obtained by the informationprocessing apparatus provided by the related art is not high, whichmakes wrong opinion information become “noise”, and too much “noise”results in that the product providers cannot make accurate judgment onthe product market reaction, and also results in that the consumerscannot correctly select required products according to the opinioninformation.

SUMMARY

Various embodiments of the present invention provide an informationprocessing method and apparatus, which can enhance precision ofextracting opinion information from an evaluation text.

The technical solution of the embodiments of the present invention isimplemented as follows:

The various embodiments of the present invention provide an informationprocessing method, the method including:

training a Deep Neural Network (DNN) by using an evaluation object seed,an evaluation term seed, and an evaluation relationship seed, the DNNincluding a first input layer, a first hidden layer, and a first outputlayer, the number of nodes of the first input layer corresponding tothat of the first output layer, and the number of nodes of the firstinput layer being greater than that of the first hidden layer;

at the first input layer, connecting vectors corresponding to acandidate evaluation object, a candidate evaluation term, and acandidate evaluation relationship to obtain a first input vector, at thefirst hidden layer, compressing the first input vector to obtain a firstmiddle vector, and at the first output layer, decoding the first middlevector to obtain a first output vector; and

determining whether a decoding error value of the first output vector isless than a decoding error value threshold, if yes, determining thecandidate evaluation object, the candidate evaluation term, and thecandidate evaluation relationship corresponding to the first outputvector as first opinion information.

Training a DNN by using an evaluation object seed, an evaluation termseed, and an evaluation relationship seed includes:

at the first input layer, connecting vectors corresponding to theevaluation object seed, the evaluation term seed, and the evaluationrelationship seed to obtain a second input vector;

at the first hidden layer, compressing the second input vector to obtaina second middle vector; and

updating a parameter set of the DNN until a Euclidean distance between asecond output vector and the second input vector is at the minimum, thesecond output vector being a vector obtained by decoding the secondmiddle vector at the first output layer.

The method further includes:

sorting evaluation objects in the first opinion information according tothe following dimensions in descending order. The number of times theevaluation objects in the first opinion information occur in anevaluation text, and the number of times the evaluation objects in thefirst opinion information are identified as evaluation object positiveexamples;

selecting M evaluation objects, in descending order, from the evaluationobjects sorted in descending order, and determining the selected Mevaluation objects as a subset of the evaluation object seed, M being aninteger greater than 1; and

training the DNN by using the evaluation object seed, the evaluationterm seed, and the evaluation relationship seed after updating theparameter set of the DNN.

After training the DNN by using the evaluation object seed, theevaluation term seed, and the evaluation relationship seed afterupdating the parameter set of the DNN, the method further includes:

at the first input layer of the DNN after updating the parameter set ofthe DNN, connecting vectors corresponding to the candidate evaluationobject, the candidate evaluation term, and the candidate evaluationrelationship to obtain a third input vector, at the first hidden layerof the DNN after updating the parameter set of the DNN, compressing thethird input vector to obtain a third middle vector, and at the firstoutput layer of the DNN after updating the parameter set of the DNN,decoding the third middle vector to obtain a third output vector; and

determining whether a decoding error value of the third output vector isless than a decoding error value threshold, if yes, determining thecandidate evaluation object, the candidate evaluation term and thecandidate evaluation relationship corresponding to the third outputvector as second opinion information.

Before determining whether the decoding error value of the first outputvector is less than the decoding error value threshold, the methodfurther includes:

determining the decoding error value threshold according to thefollowing dimensions:

target precision of opinion information extracted from an evaluationtext and target quantity of the opinion information extracted from theevaluation text; wherein

the decoding error value threshold is negatively correlated with thetarget precision, and is positively correlated with the target quantity.

At the first input layer of the DNN, before connecting vectorscorresponding to the candidate evaluation object, the candidateevaluation term, and the candidate evaluation relationship, the methodfurther includes:

mapping the candidate evaluation object and the candidate evaluationterm into corresponding vectors by using the DNN; and

at a second hidden layer to an nth hidden layer of the DNN, recursivelymapping objects included in a syntactic dependency path of theevaluation relationship into vectors; wherein

a Euclidean distance between any two vectors in the vectors obtainedthrough mapping is positively correlated with a semantic or syntacticlikelihood ratio test (LRT) index of the any two vectors, n is apositive integer, and n corresponds to the number of the objectsincluded in the syntactic dependency path of the evaluationrelationship.

At the first input layer of the DNN, before connecting vectorscorresponding to the candidate evaluation object, the candidateevaluation term, and the candidate evaluation relationship, the methodfurther includes:

extracting nouns from an evaluation text;

determining a likelihood ratio test (LRT) index between an initialevaluation object seed and the nouns extracted from the evaluation text;and

determining nouns in the extracted nouns as a subset of the evaluationobject seed, wherein the LRT index between the determined nouns and theinitial evaluation object seed is greater than an LRT index threshold.

Various embodiments of the present invention further provide aninformation processing apparatus, including:

a training unit, configured to train a DNN by using an evaluation objectseed, an evaluation term seed, and an evaluation relationship seed, theDNN including a first input layer, a first hidden layer, and a firstoutput layer, the number of nodes of the first input layer correspondingto that of the first output layer, and the number of nodes of the firstinput layer being greater than that of the first hidden layer;

a connection unit, configured to, at the first input layer, connectvectors corresponding to a candidate evaluation object, a candidateevaluation term and a candidate evaluation relationship to obtain afirst input vector;

a compression unit, configured to, at the first hidden layer, compressthe first input vector to obtain a first middle vector;

a decoding unit, configured to, at the first output layer, decode thefirst middle vector to obtain a first output vector; and

a first determination unit, configured to determine whether a decodingerror value at the first output vector is less than a decoding errorvalue threshold, if yes, determine the candidate evaluation object, thecandidate evaluation term, and the candidate evaluation relationshipcorresponding to the first output vector as first opinion information.

The connection unit is further configured to, at the first input layer,connect vectors corresponding to the evaluation object seed, theevaluation term seed, and the evaluation relationship seed to obtain asecond input vector;

the compression unit is further configured to, at the first hiddenlayer, compress the second input vector to obtain a second middlevector;

the decoding unit is further configured to, at the first output layer,decode the second middle vector to obtain a second output vector; and

the training unit is further configured to update a parameter set of theDNN until a Euclidean distance between the second output vector and thesecond input vector is minimum.

The apparatus further includes:

a sorting unit, configured to sort evaluation objects in the firstopinion information according to the following dimensions in descendingorder. The number of times the evaluation objects in the first opinioninformation occur in an evaluation text and the number of times theevaluation objects in the first opinion information are identified asevaluation object positive examples;

an updating unit, configured to select M evaluation objects, indescending order, and, from the evaluation objects sorted in descendingorder, determine the selected M evaluation objects as a subset of theevaluation object seed, M being an integer greater than 1; and

the training unit is further configured to train the DNN by using theevaluation object seed, the evaluation term seed, and the evaluationrelationship seed after updating the parameter set of the DNN.

The connection unit is further configured to, at the first input layerof the DNN after updating, connect vectors corresponding to thecandidate evaluation object, the candidate evaluation term, and thecandidate evaluation relationship to obtain a third input vector;

the compression unit is further configured to, at the first hidden layerof the DNN after updating the parameter set of the DNN, compress thethird input vector to obtain a third middle vector;

the decoding unit is further configured to, at the first output layer ofthe DNN after updating the parameter set of the DNN, decode the thirdmiddle vector to obtain a third output vector; and

the first determination unit is further configured to determine whethera decoding error value of the third output vector is less than adecoding error value threshold and, if yes, determine the candidateevaluation object, the candidate evaluation term, and the candidateevaluation relationship corresponding to the third output vector assecond opinion information.

The first determination unit is further configured to, beforedetermining whether the decoding error value of the first output vectoris less than the decoding error value threshold, determine the decodingerror value threshold according to the following dimensions:

target precision of opinion information extracted from an evaluationtext and target quantity of the opinion information extracted from theevaluation text; wherein

the decoding error value threshold is negatively correlated with thetarget precision, and is positively correlated with the target quantity.

The apparatus further includes:

a first mapping unit, configured to map the candidate evaluation objectand the candidate evaluation term into corresponding vectors by usingthe DNN; and

a second mapping unit, configured to, at a second hidden layer to an nthhidden layer of the DNN, recursively map objects included in a syntacticdependency path of the evaluation relationship into vectors; wherein

a Euclidean distance between any two vectors in the vectors obtainedthrough mapping is positively correlated with a semantic or syntacticLRT index of the any two vectors, n is a positive integer, and ncorresponds to the number of the objects included in the syntacticdependency path of the evaluation relationship.

The apparatus further includes:

an extraction unit, configured to extract nouns from an evaluation text;and

a second determination unit, configured to determine an LRT indexbetween an initial evaluation object seed and the nouns extracted fromthe evaluation text and determine nouns in the extracted nouns as asubset of the evaluation object seed, wherein the LRT index between thedetermined nouns and the initial evaluation object seed is greater thanan LRT index threshold.

In various embodiments of the present disclosure, vectors correspondingto an evaluation object, an evaluation term, and an evaluationrelationship are connected at a first input layer of a DNN and are inputto a first hidden layer, and as the number of nodes of the first hiddenlayer is less than that of the first input layer, it is equivalent tocompressing semantic or syntactic features represented by each dimensionof the input vectors at the first hidden layer. When the vectors areoutput to a first output layer for computing, as the number of nodes ofthe first output layer corresponds to that of the first input layer, itis equivalent to decoding and outputting a middle vector aftercompression at the hidden layer, and during implementation of thepresent disclosure, the smaller a decoding error value of a vector is,the greater the probability that the evaluation object, the evaluationterm, and the evaluation relationship corresponding to the vector arecorrect opinion information, so that extraction precision of opinioninformation can be controlled according to the decoding error value,achieving an effect of enhancing precision of extracting opinioninformation from an evaluation text.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of implementation of an information processingmethod according to various embodiments of the present disclosure;

FIG. 2a is a first function structure diagram of an informationprocessing apparatus according to various embodiments of the presentdisclosure;

FIG. 2b is a second function structure diagram of an informationprocessing apparatus according to various embodiments of the presentdisclosure;

FIG. 2c is a third function structure diagram of an informationprocessing apparatus according to various embodiments of the presentdisclosure;

FIG. 2d is a fourth function structure diagram of an informationprocessing apparatus according to various embodiments of the presentdisclosure;

FIG. 3 is a topological diagram of connection between an informationprocessing apparatus and an evaluation platform according to variousembodiment of the present disclosure;

FIG. 4 is a diagram of a syntactic dependency path according to variousembodiments of the present disclosure;

FIG. 5 is a diagram of word vector learning according to variousembodiments of the present disclosure;

FIG. 6 is a first diagram of obtaining a vector corresponding to anevaluation relationship by using a DNN according to various embodimentsof the present disclosure;

FIG. 7 is a second diagram of obtaining a vector corresponding to anevaluation relationship by using a DNN according to various embodimentsof the present disclosure;

FIG. 8 is a diagram of extracting opinion information by using a DNNaccording to various embodiments of the present disclosure; and

FIG. 9a -FIG. 9d are diagrams of comparison between precision ofextracting opinion information by using a DNN and precision ofextracting the opinion information by using the related art according tovarious embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

In order to make the objectives, technical solutions and advantages ofthe present disclosure more comprehensible, the present disclosure isfurther described below in detail with reference to accompanyingdrawings and embodiments. It should be understood that specificembodiments described herein are merely used to explain the presentdisclosure, but not intended to limit the present disclosure.

During implementation of the present disclosure, the inventor findsthat, in the related art, opinion information obtained by an informationprocessing apparatus includes evaluation objects (generally functions orattributes of a product), evaluation terms (words expressing useropinion polarity, which can be understood as polarity sentiment words),and evaluation relationships (that is, modification relationshipsbetween the evaluation terms and the evaluation objects). The inventoralso finds the problem with the precision rate of opinion informationoutput by the information processing apparatus in the related art is nothigh, as shown in at least the following examples.

Incorrect identification of evaluation terms. That is, words notexpressing user opinion polarity are identified as evaluation terms. Forexample, if an evaluation text in an mp3 field (i.e., the evaluationtext is from an evaluation platform of an mp3 product) includes anevaluation text “Just another mp3 I bought”, the information processingapparatus, when processing the evaluation text according to a syntacticdependency path, registers that the word closest to an evaluation objectin a sentence is an evaluation term, therefore, when “mp3” is used as anevaluation object seed to search for an evaluation term in theevaluation text, for the evaluation text, it is determined that“another” is an evaluation term that modifies the evaluation object“mp3”, and as the evaluation term “another” is not a polarity sentimentword (e.g., good, bad), it results in that noise opinion information isoutput, thereby affecting precision of extracting opinion information.

Similarly, in the related art, when the information processing apparatususes a co-occurrence statistical manner to determine opinioninformation, if, in the evaluation text in the mp3 field, a ratio of thenumber of times “another” and the evaluation object seed “mp3” co-occurin the evaluation text (set as a) to the sum of the number of times“another” occurs alone in the evaluation text (set as b) and the numberof times “mp3” occurs alone in the evaluation text (set as c), that is,a/(b+c), exceeds a preset threshold, it is determined that “another” and“mp3” are elements in the opinion information, it is determinedaccording to part of speech that “another” is used for evaluating “mp3”,and the corresponding opinion information is “another mp3”, whichresults in that noise opinion information is extracted, therebyaffecting precision of extracting opinion information.

Incorrect identification of evaluation objects. That is, a word modifiedby an evaluation object is identified incorrectly. For example, for anevaluation text “the mp3 has many good things”, the informationprocessing apparatus, when processing the evaluation text based on asyntactic dependency path, thinks that the word closest to an evaluationobject in a sentence is an evaluation term, that is, an evaluationrelationship exists between the word closest to the evaluation objectand the evaluation object. Therefore, when “good” is used as anevaluation term seed to look for an evaluation object in the evaluationtext in the mp3 field (i.e., the evaluation text is an evaluation textof an mp3 product), for the evaluation text, it is determined that theobject modified by “good”, i.e., the evaluation object, is “things”,however, things is a word irrelevant to the field of mp3 products, whichthus generates noise opinion information.

Similarly, in the related art, when the information processing apparatususes a co-occurrence statistical manner to determine opinioninformation, when “mp3” is used as an evaluation term seed to look foran evaluation object in the evaluation text of mp3, if a ratio of thenumber of times “another” and “mp3” co-occur in the evaluation text tothe number of times “another” and “mp3” occur separately in theevaluation text exceeds a preset threshold, it is determined that“another” and “mp3” are elements in the opinion information, it isdetermined according to part of speech that “another” is used forevaluating “mp3”, and the corresponding opinion information is “anothermp3”, which thus generates noise opinion information.

In combination with the analysis, in the related art, during extractionof opinion information, only any two in an evaluation term, anevaluation object, and an evaluation relationship are verified in anevaluation text, thus resulting in the problem of low precision ofextraction of opinion information. For example, when an evaluation termis determined according to an evaluation object seed term, it isdetermined that the evaluation term is used for modifying the evaluationobject seed term, but whether a modification relationship between thedetermined evaluation term and the evaluation object seed term iscorrect has not been verified. Likewise, when the evaluation object isdetermined according to an evaluation term seed, it is determined thatthe evaluation term seed is used for modifying the determined evaluationobject, but whether a modification relationship between the evaluationterm seed and the determined evaluation object is correct is notverified, resulting in that the information processing apparatus in therelated art has a problem that extraction precision of opinioninformation is not high. Accordingly, the precision of extracting theopinion information can be improved significantly if the evaluationterm, the evaluation object, and the evaluation relationship can beverified at the same time when the opinion information is extracted fromthe evaluation text.

Various embodiments of the present disclosure describe an informationprocessing method, which can verify evaluation terms, evaluationobjects, and evaluation relationships included in an evaluation text atthe same time and has high precision of extraction of opinioninformation. As shown in FIG. 1, the information processing methoddescribed in the various embodiments include the following blocks.

Block 101: Train a DNN by using an evaluation object seed, an evaluationterm seed, and an evaluation relationship seed.

The DNN includes a first input layer, a first hidden layer and a firstoutput layer, the number of nodes of the first input layer correspond tothat of the first output layer, and the number of nodes of the firstinput layer are greater than that of the first hidden layer.

The evaluation object seed, the evaluation term seed, and the evaluationrelationship seed can correspond to a different product field (such asMP3 or Phone), and the evaluation object seed, the evaluation term seed,and the evaluation relationship seed constitute correct opinioninformation (opposite to noise opinion information) of a product in acorresponding field. Therefore, seeds can also be seen as positiveexamples, and the evaluation object seed, the evaluation term seed, andthe evaluation relationship seed constitute opinion information positiveexamples.

Block 102: At the first input layer, connect vectors corresponding to acandidate evaluation object, a candidate evaluation term, and acandidate evaluation relationship to obtain a first input vector.

The candidate evaluation object, the candidate evaluation term, and thecandidate evaluation relationship constitute candidate opinioninformation, and the various embodiments are aimed at extracting correctopinion information from the candidate opinion information. Thecandidate evaluation object and the candidate evaluation term can beobtained through extraction from an evaluation text. For example, verbsand adjectives can be extracted from the evaluation text as candidateevaluation terms, and nouns are extracted from the evaluation text ascandidate evaluation objects. The candidate evaluation relationship canbe determined based on a syntactic dependency path of the evaluationtext.

Vectors corresponding to the candidate evaluation object and thecandidate evaluation term can be obtained through word vector learningby using the DNN, the word vector learning refers to mapping thecandidate evaluation object and the candidate evaluation term to ahigh-dimensional space to obtain vectors, and each dimension of thevectors represents a syntactic or semantic feature of the term. Thevarious embodiments carry out word vector learning according to thefollowing strategy, i.e., when two words are syntactically orsemantically closer, a Euclidean distance between word vectors of thetwo words is smaller.

The vector corresponding to the candidate evaluation relationship can beobtained by recursively mapping the evaluation relationship according toa syntactic dependency path at a second hidden layer to an nth hiddenlayer of the DNN. Wherein a Euclidean distance between any two vectorsin the vectors obtained through mapping is positively correlated with asemantic or syntactic LRT index of the any two vectors, n is a positiveinteger, and n corresponds to the number of the objects included in thesyntactic dependency path of the evaluation relationship.

Block 103: At the first hidden layer, compress the first input vector toobtain a first middle vector, and at the first output layer, decode thefirst middle vector to obtain a first output vector.

As the number of nodes (also referred to as computing units) of thefirst hidden layer are less than that of the first input layer and eachnode of the first hidden layer only carries an operation of onedimension of a vector, semantic or syntactic features of the vectoroutput by the first input layer to the first hidden layer can becompressed, as the number of nodes of the first output layer are thesame as that of the first input layer, the middle vector output by thefirst hidden layer to the first output layer can be decoded.

Block 104: Determine whether a decoding error value of the first outputvector is less than a decoding error value threshold, if yes, determinethe candidate evaluation object, the candidate evaluation term, and thecandidate evaluation relationship corresponding to the first outputvector as first opinion information.

During implementation of the present disclosure, if a vectorcorresponding to an opinion information positive example (i.e., thevector formed by connecting the vectors corresponding to the evaluationobject seed, the evaluation term seed, and the evaluation relationshipseed) is input at the first input layer, as the closer semantic orsyntactic features between vectors are, the smaller the Euclideandistance between the vectors is, therefore, semantic or syntacticfeatures of the opinion information positive example can be well decodedat the first output layer (that is, a less decoding error value will beobtained). Alternately, if an opinion information negative example(i.e., noise opinion information) is input at the first input layer, adecoding error value of the vector output at the first output layer willbe greater than that of the vector corresponding to the opinioninformation positive example, thus leading to failure of decoding at theoutput layer. Accordingly, the process of extracting opinion informationfrom an evaluation text can be converted to a process of, at the firstinput layer, inputting the first input vectors corresponding to thecandidate evaluation term, the candidate evaluation object, and thecandidate evaluation relationship, and verifying whether a decodingerror value of the first output vector at the first output layer issmall. For example, the candidate evaluation term, the candidateevaluation object, and the candidate evaluation relationshipcorresponding to the vector in the first output vectors decoded andoutput whose decoding error value is less than a decoding error valuethreshold can be output as opinion information positive examples (i.e.,first opinion information).

Before the first input vector corresponding to the candidate evaluationterm, the candidate evaluation object, and the candidate evaluationrelationship is input at the first input layer, it is necessary to usethe evaluation term seed, the evaluation object seed, and the evaluationrelationship seed to train the DNN, so that the DNN has good performanceof correctly decoding semantic or syntactic features of vectorscorresponding to positive examples at the first output layer (i.e., thedecoding error value is small).

As one example, the DNN can be trained in the following manner. At thefirst input layer, connecting vectors corresponding to the evaluationobject seed, the evaluation term seed, and the evaluation relationshipseed to obtain a second input vector; at the first hidden layer,compressing the second input vector to obtain a second middle vector,and updating a parameter set of the DNN until a Euclidean distancebetween a second output vector and the second input vector is minimum,the second output vector being a vector obtained by decoding the secondmiddle vector at the first output layer.

In application, evaluation object seeds inevitably have a problem ofsparse distribution, thus resulting in that first opinion informationextracted from the evaluation text being incomplete. In view of this,evaluation objects in the first opinion information can be sortedaccording to the following dimensions in descending order. The number oftimes the evaluation objects in the first opinion information occur inan evaluation text and the number of times the evaluation objects in thefirst opinion information are identified as evaluation object positiveexamples, selecting M (M is an integer greater than 1) evaluationobjects, in descending order, from the evaluation objects sorted indescending order, and determining the selected M evaluation objects as asubset of the evaluation object seed, equivalent to expanding theevaluation object seeds according to the opinion information (i.e.,first opinion information) extracted from the evaluation text for thefirst time. In this way, re-training the DNN by using the evaluationterm seed, the evaluation relationship seed, and the evaluation objectseed after updating the parameter set of the DNN can make the DNN havepositive performance of correctly decoding semantic or syntacticfeatures of vectors corresponding to positive examples at the firstoutput layer.

Correspondingly, it is feasible to, at the first input layer of the DNNafter updating the parameter set of the DNN, connect vectorscorresponding to the candidate evaluation object, the candidateevaluation term and the candidate evaluation relationship to obtain athird input vector, at the first hidden layer of the DNN after updatingthe parameter set of the DNN, compress the third input vector to obtaina third middle vector, and at the first output layer of the DNN afterupdating the parameter set of the DNN, decode the third middle vector toobtain a third output vector and determine whether a decoding errorvalue of the third output vector is less than a decoding error valuethreshold. If yes, determine the candidate evaluation object, thecandidate evaluation term and the candidate evaluation relationshipcorresponding to the third output vector as second opinion information.As the second opinion information is obtained based on the re-trainedDNN, the second opinion information is more comprehensive relative tothe first opinion information, that is, compared with the first opinioninformation, the second opinion information includes more candidateevaluation term positive examples, candidate evaluation object positiveexamples, and candidate evaluation relationship positive examples.

In order to further overcome the problem of sparse distribution of theevaluation object seeds, at the first input layer of the DNN, beforevectors corresponding to the evaluation object, the evaluation term, andthe evaluation relationship are connected to train the DNN, nouns can beextracted from the evaluation text, an LRT index between an initialevaluation object seed and the nouns extracted from the evaluation textis determined, and nouns in the extracted nouns the LRT index betweenwhich and the initial evaluation object seed is greater than an LRTindex threshold are determined as a subset of the evaluation objectseed, equivalent to expanding the evaluation object seed based on theevaluation text, as the expanded evaluation object seed is an evaluationobject positive example acquired from the evaluation text, training theDNN by using the expanded evaluation object seed, the evaluation termseed and the evaluation relationship seed can make the DNN have goodperformance of correctly decoding semantic or syntactic features ofvectors corresponding to positive examples at the first output layer.

During actual applications, the decoding error value threshold can bedetermined according to the following dimensions:

target precision of opinion information extracted from an evaluationtext and target quantity of the opinion information extracted from theevaluation text, wherein the decoding error value threshold isnegatively correlated with the target precision, and is positivelycorrelated with the target quantity. That is to say, the smaller thedecoding error value threshold is, the higher the precision of the firstopinion information and the second opinion information is, as thedecoding error value threshold becomes less, equivalent to improvingrequirements for decoding precision, the fewer the first output vectorsless than the decoding error value obtained at the first output layerare, and correspondingly, the number of the first opinion information(or the second opinion information) is less; and vice versa.

In various embodiments of the present disclosure, vectors correspondingto an evaluation object, an evaluation term, and an evaluationrelationship are connected at a first input layer of a DNN and are inputto a first hidden layer, and as the number of nodes of the first hiddenlayer is less than that of the first input layer, it is equivalent tocompressing semantic or syntactic features represented by each dimensionof the input vectors at the first hidden layer, when the vectors areoutput to a first output layer for computing, as the number of nodes ofthe first output layer corresponds to that of the first input layer, itis equivalent to decoding and outputting a middle vector aftercompression at the hidden layer, and during implementation of thepresent disclosure, the inventor finds that the smaller a decoding errorvalue of a vector is, the greater the probability that the evaluationobject, the evaluation term, and the evaluation relationshipcorresponding to the vectors are correct opinion information is, so thatextraction precision of opinion information can be controlled accordingto the decoding error value, achieving an effect of enhancing precisionof extracting opinion information from an evaluation text. At the sametime, vectors are formed by connecting vectors corresponding to thecandidate evaluation object, the candidate evaluation term, and thecandidate evaluation relationship, which is equivalent to verifying theevaluation object, the evaluation term, and the evaluation relationshipsimultaneously by using a decoding error threshold during extraction ofopinion information, and precision of the extraction of opinioninformation is definitely higher than that in the related art.

The various embodiments of the present disclosure further describe acomputer storage medium, the computer storage medium stores a computerexecutable instruction, and the computer executable instruction is usedfor performing the information processing method shown in FIG. 1.

The various embodiments of the present disclosure further describe aninformation processing apparatus, as shown in FIG. 2, including:

a training unit 21, configured to train a DNN by using an evaluationobject seed, an evaluation term seed, and an evaluation relationshipseed, the DNN including a first input layer, a first hidden layer, and afirst output layer, the number of nodes of the first input layercorresponding to that of the first output layer, and the number of nodesof the first input layer being greater than that of the first hiddenlayer;

a connection unit 22, configured to, at the first input layer, connectvectors corresponding to a candidate evaluation object, a candidateevaluation term and a candidate evaluation relationship to obtain afirst input vector;

a compression unit 23, configured to, at the first hidden layer,compress the first input vector to obtain a first middle vector;

a decoding unit 24, configured to, at the first output layer, decode thefirst middle vector to obtain a first output vector; and

a first determination unit 25, configured to determine whether adecoding error value of the first output vector is less than a decodingerror value threshold, if yes, determine the candidate evaluationobject, the candidate evaluation term and the candidate evaluationrelationship corresponding to the first output vector as first opinioninformation.

As one implementation, the connection unit 22 is further configured to,at the first input layer, connect vectors corresponding to theevaluation object seed, the evaluation term seed, and the evaluationrelationship seed to obtain a second input vector;

the compression unit 23 is further configured to, at the first hiddenlayer, compress the second input vector to obtain a second middlevector;

the decoding unit 24 is further configured to, at the first outputlayer, decode the second middle vector to obtain a second output vector;and

the training unit 25 is further configured to update a parameter set ofthe DNN until a Euclidean distance between the second output vector andthe second input vector is minimum.

As one implementation, as shown in FIG. 2b , on the basis of theapparatus shown in FIG. 2a , the apparatus may further include:

a sorting unit 25, configured to sort evaluation objects in the firstopinion information according to the following dimensions in descendingorder. The number of times the evaluation objects in the first opinioninformation occur in an evaluation text; and the number of times theevaluation objects in the first opinion information are identified asevaluation object positive examples;

an updating unit 26, configured to select M evaluation objects, indescending order, from the evaluation objects sorted in descendingorder, and determine the selected M evaluation objects as a subset ofthe evaluation object seed, M being an integer greater than 1; and

the training unit 21 is further configured to train the DNN by using theevaluation object seed, the evaluation term seed and the evaluationrelationship seed after updating the parameter set of the DNN.

As one implementation, the connection unit 22 is further configured to,at the first input layer of the DNN after updating the parameter set ofthe DNN, connect vectors corresponding to the candidate evaluationobject, the candidate evaluation term and the candidate evaluationrelationship to obtain a third input vector;

the compression unit 23 is further configured to, at the first hiddenlayer of the DNN after updating the parameter set of the DNN, compressthe third input vector to obtain a third middle vector;

the decoding unit 24 is further configured to, at the first output layerof the DNN after updating the parameter set of the DNN, decode the thirdmiddle vector to obtain a third output vector; and

the first determination unit 25 is further configured to determinewhether a decoding error value of the third output vector is less than adecoding error value threshold, if yes, determine the candidateevaluation object, the candidate evaluation term and the candidateevaluation relationship corresponding to the third output vector assecond opinion information.

As one implementation, the first determination unit 25 is furtherconfigured to, before determining the first output vector whose decodingerror value is less than a decoding error value threshold, determine thedecoding error value threshold according to the following dimensions:

target precision of opinion information extracted from an evaluationtext; and target quantity of the opinion information extracted from theevaluation text; wherein the decoding error value threshold isnegatively correlated with the target precision, and is positivelycorrelated with the target quantity.

As one implementation, as shown in FIG. 2c , on the basis of theapparatus shown in FIG. 2a , the apparatus further includes:

a first mapping unit 27, configured to map the candidate evaluationobject and the candidate evaluation term into corresponding vectors byusing the DNN; and

a second mapping unit 28, configured to, at a second hidden layer to annth hidden layer of the DNN, recursively map objects included in asyntactic dependency path of the evaluation relationship into vectors;wherein

a Euclidean distance between any two vectors in the vectors obtainedthrough mapping is positively correlated with a semantic or syntacticLRT index of the any two vectors, n is a positive integer, and ncorresponds to the number of the objects included in the syntacticdependency path of the evaluation relationship.

As one implementation, as shown in FIG. 2d , on the basis of theapparatus shown in FIG. 2a , the apparatus may further include:

an extraction unit 29, configured to extract nouns from the evaluationtext; and

a second determination unit 210, configured to determine an LRT indexbetween an initial evaluation object seed and the nouns extracted fromthe evaluation text; and determine nouns in the extracted nouns the LRTindex between which and the initial evaluation object seed is greaterthan an LRT index threshold as a subset of the evaluation object seed.

It should be noted that, the technical features of informationprocessing performed by the information processing apparatus correspondto those described in the method embodiment, and reference can be madeto the description in the method embodiment for details not disclosed inthe apparatus embodiment.

It should be noted that, the information processing apparatus describedin the embodiment of the present invention can be run in one or moreservers, and units in the information processing apparatus can beimplemented by a Central Processing Unit (CPU) and a co-processingcomputer card in the information processing apparatus.

Description is given below in combination with an actual processingscenario, as shown in FIG. 3, the information processing apparatus(which can also be regarded as an opinion mining apparatus) is in a dataconnection with a server where an evaluation platform is run, so as toacquire an evaluation text in a certain field from the server of theevaluation platform to carry out opinion mining and output opinioninformation, herein, the evaluation platform may be any productevaluation platform (such as Taobao's or Amazon's evaluation platform),as the current evaluation platform always stores evaluation informationof products based on categories and models of the products, theinformation processing apparatus described in the various embodiments ofthe present disclosure can directly acquire an evaluation text in acertain field from a server where an evaluation platform is run, andprocessing performed by the information processing apparatus isdescribed below.

Step 1: Acquire seeds. It is necessary to acquire seeds before theevaluation text from the evaluation platform is processed. The acquiredseeds include an evaluation term seed, an evaluation object seed, and anevaluation relationship seed. The evaluation term seed, the evaluationobject seed, and the evaluation relationship seed can constitute opinioninformation (also referred to as evaluation phrase).

For the evaluation term seed, the information processing apparatusacquires independent evaluation terms in 186 fields (corresponding todifferent products) from a SentiWordNet as an evaluation term seed setOS.

For the evaluation object seed, the information processing apparatususes an LRT index to determine the evaluation object seed.

By taking an evaluation object seed in the mp3 field as an example,first, the information processing apparatus takes a noun or a nounphrase as an evaluation object, and uses a Tokenizer (such as StanfordTokenizer) to word-segment the evaluation text, to obtain a noun set{T_(i)};

second, based on an initial evaluation object seed set {T_(i)} in themp3 field (T_(i) corresponds to product attributes of mp3, for example,storage capacity, size, hardware configuration and the like), an LRTindex between T_(i) and a noun set {T_(j)} extracted from the evaluationtext in the mp3 field are determined;

next, a preset number of T_(j) with the highest LRT index are combinedwith an initial object seed set {T_(i)}, to serve as an evaluationobject seed set TS; the LRT index reflects correlation between T_(j) andan initial evaluation object seed T, that is to say, the higher thecorrelation between T_(j)s is, the greater the probability that T_(j)represents product attributes in the mp3 field is, therefore, in thevarious embodiments of the present disclosure, the initial evaluationobject seed set {T_(i)} in the mp3 field is expanded according to themp3 evaluation text, so as to determine corresponding evaluation objectsaccording to different evaluation texts in actual applications, whichovercomes the defect that an evaluation object distribution coefficientcauses extracted opinion information to be one-sided. One example of theLRT index is as shown in Formula (1) below:LRT=2 log L(p ₁ ,k ₁ ,n ₁)+log L(p ₂ ,k ₂ ,n ₂)−log L(p,k ₁ ,n ₁)−log{p,k ₂ ,n ₂}  (1)

where k₁=tf (T_(i),T_(j)), k₂=tf (T_(i),T _(j)), k₃=tf (T _(i),T_(j)),k₄=tf (T _(i),T _(j)), and tf ( ) denotes the occurrence frequency;L(p,k,n)=p^(k) (1−p)^(n-k), n₁=k₁+k₃, n₂=k₂+k₄, p₁=k₁/n₁, and p₁=k₂/n₂.

It should also be noted that, for the evaluation text, as evaluationobjects may occur in a multiple-word-connection form, the informationprocessing apparatus may segment a noun word group in the evaluationtext into single nouns, it is necessary to first detect noun phrases inthe evaluation text and combine the phrases into one term. Inapplication, the noun phrases can be determined based on a co-occurrencestatistical manner, that is, multiple continuous nouns whose occurrencefrequency exceeds a certain threshold are connected into a noun phraset, and one example of the occurrence frequency is as shown in Formula(2) below:

CValue(t) = log (t) × t f(t)  when  t  is  included   by   another   phrase${{{CValue}(t)} = {{{\log\left( {t} \right)} \times t\;{f(t)}} - {\frac{1}{n(L)}{\sum\limits_{l \in L}\;{t\;{f(l)}}}}}}\mspace{14mu}$when  t   is  not   included  by  another  phrase

where |t| denotes the number of terms included in t, tf (t) denotes thenumber of times t occurs in the evaluation text, L denotes all possiblephrase sets including t, and n (L) indicates the number of the sets L.

For the evaluation relationship seed, as an evaluation relationship inthe evaluation text can be represented with a syntactic dependency in asentence of the evaluation text, the information processing apparatuscan extract an evaluation relationship template from the evaluation textas an initial evaluation relationship seed set RS, a description isgiven as follows.

First, syntactic dependency analysis is performed on all sentences inthe evaluation text, and then an evaluation relationship template isextracted from a syntactic dependency path through a template extractiontechnology (e.g., evaluation relationship graph walk algorithm). FIG. 4illustrates a syntactic dependency path of an example sentence “Thestyle of the screen is gorgeous”, in order to extract the evaluationrelationship template, terms in the sentence are replaced with wildcardcharacters at first, herein, a wildcard character <TC> is used torepresent a candidate evaluation object, <OC> is used to represent acandidate evaluation term, “gorgeous” and “screen” in FIG. 4 arerespectively replaced with <OC> and <TC>, and other words in the syntaxtree are replaced with corresponding parts of speech tags. Descriptionis given below with a template example.

TC-TC template: The template corresponds to a situation where two,juxtaposed evaluation objects are extracted, for example, in an examplesentence “both color and style of the dress are pretty good”, color andstyle are two juxtaposed evaluation objects.

OC-TC template: The template corresponds to a situation where oneevaluation term modifies one evaluation object, for example, in anexample sentence “fabrics of the dress are great”, “fabrics” is anevaluation object and “great” is an evaluation term.

OC-OC template: The template corresponds to a situation where twoevaluation terms are juxtaposed.

In the process that the information processing apparatus extracts atemplate, the shortest path from a candidate evaluation term <OC> to acandidate evaluation object <TC> in the syntax tree is taken as anevaluation relationship template, referring to FIG. 3, the shortest pathbetween two <TC> in FIG. 3 is <TC>-{mod}-(Prep)-{pcomp-n}-<TC>, whichcorresponds to a TC-TC template; the shortest path from <OC> to <TC> inFIG. 3 is <OC>-{pred}-(VBE)-{s}<TC>, which corresponds to an OC-TCtemplate.

During actual applications, the longer the template extracted by theinformation processing apparatus is, the lower the semantic accuracyexpressed by the template is. Therefore, the information processingapparatus can extract the template according to a template lengththreshold, for example, when the template length threshold is 4, thetemplate extracted by the information processing apparatus includes 4terms at most. The information processing apparatus can also sort theextracted templates according to occurrence frequency in the evaluationtext, and takes a template whose occurrence frequency exceeds a presetthreshold as an evaluation relationship template seed.

As the opinion information is in a phrase form, the opinion informationis equivalent to an evaluation phrase. Correspondingly, in the variousembodiments of the present disclosure, the information processingapparatus can use the following manner to indicate the obtained seedset: s_(e)={s_(o),s_(t),s_(r)}, where s_(o)∈OS, s_(t)∈TS, and s_(r)∈RS.

Step 2: Extract candidate opinion information based on an evaluationtext

In the various embodiments of the present disclosure, one opinioninformation is represented in the following manner, e={o,t,r}; where ois an evaluation term (words expressing user opinion polarity, which canbe understood as polarity sentiment words), t is an evaluation object(generally functions or attributes of a product), and r (i.e., theevaluation term is used for modifying the evaluation object) indicatesthat the evaluation term o is used for modifying the evaluation objectt. In one example of the opinion information, in “this mp3 has clearscreen”, o={clear}, t={screen}, and r indicates that clear is used formodifying screen; as the opinion information is in a phrase form,candidate opinion information is extracted from an evaluation text (alsoreferred to as corpus), equivalent to extracting a candidate evaluationphrase in the evaluation text from the evaluation platform.

As a candidate evaluation phrase is often in a verb or adjective formand the candidate evaluation object is often in a noun form, theinformation processing apparatus described in the embodiment of thepresent invention extracts a noun from the evaluation text as acandidate evaluation object and extracts a verb or adjective as acandidate evaluation term, and a candidate evaluation object set C canbe represented in the following manner: C={OC,TC}; where OC denotes acandidate evaluation term, and TC denotes a candidate evaluation object.

Correspondingly, a candidate evaluation phrase set can be represented inthe following manner: c_(e)={c_(o),c_(t),c_(r)}, where c_(o)∈OC,c_(t)∈TC, and a candidate evaluation relationship between c_(o) andc_(i) can be determined by the information processing apparatus based ona syntactic dependency path in the evaluation text, which has beendescribed in detail in step 1 and is not repeated herein.

Step 3: Word vector learning. That is, the evaluation term seed, theevaluation object seed, the candidate evaluation object, and thecandidate evaluation term determined in the above steps are mapped intoword vectors (also referred to as word representation). A word vector isa mathematical object corresponding to each word. The word vector can berepresented in a vector form, each dimension of the word vector carriescertain potential syntactic or semantic information of the word, andword vector learning means mapping a word to a high-dimensional space toobtain a corresponding word vector. The word vector learning in the stepis aimed at when two words are syntactically or semantically closer, aEuclidean distance between word vectors of the two words is smaller.

The information processing apparatus uses a real matrix LT to representword vectors of |V_(w)| words, the number of dimensions of each wordvector is n, and correspondingly, LT_(∈)

^(n×|V) ^(w) ^(|), that is to say, LT is made up of word vectors of|V_(w)| words, the ith column of the matrix LT corresponds to a wordvector of the ith word T_(i) in the |V_(w)| words, each dimension of theith column of the matrix LT carries potential syntactic or semanticinformation of the word T_(i), and the word vector x_(i) of T_(i) can berepresented with x_(i)=LTb_(i), where b_(i)∈

^(|V) ^(w) ^(|); for the |V_(w)| words, the process that the informationprocessing apparatus obtains the real matrix LT is the training processof word vectors, when training of word vectors is completed to obtainthe matrix LT, the syntactically or semantically closer any two words inthe corresponding |V_(w)| words in the matrix LT is, the smaller theEuclidean distance between word vectors of the two words is, and inorder to meet the condition, by taking that word vector training isperformed on the candidate term set C as an example, the informationprocessing apparatus can train word vectors according to Formula (3):

$\begin{matrix}{\hat{\theta} = {\underset{\theta}{argmin}{\sum\limits_{c \in C}\;{\sum\limits_{v \in V_{w}}\;{\max\left\{ {0,{1 - {s_{\theta}(c)} + {s_{\theta}(v)}}} \right\}}}}}} & (3)\end{matrix}$

where s_(θ)(c)=the score of inputting c made by the DNN (c is acandidate evaluation term or candidate evaluation object); s_(θ)(v)s_(θ)(c)=the score of inputting a random word v made by the DNN, C={OC,TC}, θ is a parameter set of the DNN used for training word vectors, thevalue of {circumflex over (θ)} represents semantic or syntacticcorrelation between c and v, the less the value of {circumflex over (θ)}is, the greater the syntactic correlation between c and v is, therefore,when the minimum value of {circumflex over (θ)} is obtained bytraversing c and v, c and v corresponding to the minimum value of{circumflex over (θ)} are mapped into vectors with the minimum Euclideandistance.

During implementation of the present disclosure, the DNN is a veryeffective tool for allowing the information processing apparatus tocarry out word vector learning. Moreover, during actual applications, itis difficult to obtain negative examples (which do not constituteevaluation terms, evaluation objects and evaluation relationships ofopinion information in any field) in the evaluation text. Therefore, thevarious embodiments of the present disclosure use a One-ClassClassification technology, that is, only positive examples are used(corresponding to determining an evaluation object seed, an evaluationterm seed and an evaluation relationship seed in step 1 to determineopinion information in the evaluation text. That is to say, the variousembodiments of the present disclosure fuse a DNN technology and theOne-Class Classification technology into a One-Class Classification DNN(OCDNN) technology. The following describes that the informationprocessing apparatus uses the OCDNN technology to perform processing ofword vector learning, as shown in FIG. 5, by taking that an evaluationterm and an evaluation object are mapped into word vectors. As anexample, the evaluation term herein includes the evaluation term seedacquired in step 1 and the candidate evaluation term acquired in step 2,and the evaluation object includes the evaluation object seed acquiredin step 1 and the candidate evaluation object acquired in step 2; asshown in FIG. 5, each empty circle in FIG. 5 represents a computing unitof the DNN, each computing unit carries training of one dimension of aword vector of the evaluation term or evaluation object (correspondingto a solid circle), the training (corresponding to a vertical lineshadow circle) is aimed at making the Euclidean distance between wordvectors syntactically or semantically close to each other minimum;during actual applications, word vector training can be performedaccording to the standard shown in Formula (3).

Step 4: Obtain vector representation of an evaluation relationship.Similar to the processing in step 3, the step is aimed at mapping anevaluation relationship into a vector (including the evaluationrelationship seed obtained in step 1 and the candidate evaluationrelationship obtained in step 3).Aas shown in FIG. 6, the evaluationrelationship includes three objects (corresponding to solid circles),and the computing unit iteratively merges the three objects according toa syntactic dependency path (i.e., syntactic dependency between thethree objects), to map the three objects into vectors representing anevaluation relationship.

One diagram showing that an evaluation relationship is mapped into avector according to a syntactic dependency path is shown in FIG. 7. Foran example sentence “too loud to listen to the player”, the informationprocessing apparatus determines a syntactic dependency path between anevaluation term c_(o) (loud) and an evaluation object c_(t) (player), asit is only necessary to focus on syntactic dependency between objects(corresponding to words) during processing of an evaluationrelationship, c_(o) and c_(t) are correspondingly replaced with wildcardcharacters OC and TC; starting from OC, along a syntactic dependencypath, objects included in the syntactic dependency path are graduallymapped into vectors, until traversal of the syntactic dependency path iscompleted; for the syntactic dependency path shown in FIG. 7: [OC](loud)-listen-to-[TC] (player), at first, [OC] (corresponding to x₁) andlisten (corresponding to x₂) are mapped into a vector y₁, and then y₁and “to” (corresponding to x₃) are merged to obtain a vector y₂, and y₂and [TC] (corresponding to x₄) are merged to obtain a vector y₃. Thus,the evaluation relationship corresponding to the syntactic dependencypath shown in FIG. 7 is mapped into a vector y₃, and the followingdescribes specific implementation:

1) [OC] (corresponding to x₁) and listen (corresponding to x₂) aremapped into a vector y₁.

The dotted box in FIG. 2 illustrates a process of mapping OC(corresponding to x₁) and “listen” (corresponding to x₂) to an-dimension vector (corresponding to y₁), and it can be seen from FIG. 7that the number of nodes (i.e., computing units) of an input layer(corresponding to OC and “listen”) is equal to the number of nodes of anoutput layer (corresponding to y₁), as each computing unit carriestraining of one dimension of the vector, it is equivalent to compressingsyntactic or semantic features of OC (corresponding to x₁) and “listen”(corresponding to x₂) into the vector y₁, and y¹ can be represented withFormula (4):y ₁ =f(W ^((dep))[x ₁ :x ₂]+b)  (4)

where [x₁;x₂] denotes connection of vectors corresponding to OC(corresponding to x₁) and “listen” (corresponding to x₂), f ( ) denotesa sigmoid function, W^((xcomp)) denotes a parameter matrix correspondingto syntactic dependency, i.e., an object complement (xcomp)relationship, between x₁ and x₂, and an initial value of W^((xcomp)) isas shown by Formula (5):W ^((dep))=1/2[I ₁ ;I ₂ ;I _(b)]+ε  (5)

where I₁ corresponds to the syntactic dependency (i.e., the xcompbetween x₁ and x₂), I₁ denotes a n-order matrix, I₂ denotes an-dimension space vector, and ε is generated from uniform distribution U[−0.001, 0.001].

2) y₁ and “to” (corresponding to x₃) are mapped into a vector y₂.

The number of nodes (i.e., computing units) of an input layer(corresponding to y₁ and “to”) is equal to the number of nodes of anoutput layer (corresponding to y₁), as each computing unit carriestraining of one dimension of the vector, it is equivalent to compressingsyntactic or semantic features of y₁ and “to” into the vector y₂, and y₂can be represented with Formula (6):y ₂ =f(W ^((prep))[y ₁ :x ₃]+b)  (6)

where [y₁:x₃] denotes connection of vectors corresponding to y₁ and “to”(corresponding to x₃), f ( ) denotes a sigmoid function, W^((prep))denotes a parameter matrix corresponding to syntactic dependency, i.e.,a prep relationship, between y₁ and x₃, and an initial value ofW^((prep)) is as shown by Formula (7):W ^((prep))=1/2[I ₃ ;I ₄ ;I _(b)]+ε  (7)

where I₃ corresponds to the syntactic dependency (i.e., the xcompbetween x₁ and x₂), I₃ denotes a n-order matrix, I₄ denotes an-dimension space vector, and ε is generated from uniform distribution U[−0.001, 0.001].

3) y₂ and [TC] (corresponding to x₄) are mapped into a vector y₃.

The number of nodes (i.e., computing units) of an input layer(corresponding to y₂ and [TC]) is equal to the number of nodes of anoutput layer (corresponding to y₄), as each computing unit carriestraining of one dimension of the vector, it is equivalent to compressingsyntactic or semantic features of y₂ and [TC] into the vector y₃, and y₃can be represented with Formula (8):y ₃ =f(W ^((prep))[y ₂ ;x ₃]+b)  (8)

where [y₂:x₃] denotes connection of vectors corresponding to y₂ and [TC](corresponding to x₄), f ( ) denotes a sigmoid function, W^((pobj))denotes a parameter matrix corresponding to syntactic dependency, i.e.,a pobj relationship, between y₁ and x₃, and an initial value ofW^((pobj)) is as shown by Formula (9):W ^((pobj))=1/2[I ₅ ;I ₆ ;I _(b)]+ε  (9)

where I₅ corresponds to the syntactic dependency (i.e., the xcompbetween y₁ and x₃), I₆ denotes a n-order matrix, I₄ denotes an-dimension space vector, and ε is generated from uniform distribution U[−0.001, 0.001].

4) Train W^((xcomp)), W^((prep)) and W^((pobj))

Steps 1 to 3 are repeated to adjust values of W^((xcomp)), W^((prep))and W^((pobj)), to minimize a Euclidean distance between an input layerand an output layer of a recursive decoder corresponding to Formulas(10), (11) and (12);E _(rae1)=∥[x ₁ ;x ₂]−[x ₁ ′;x ₂′]∥²  (10)E _(rae2)=∥[y ₁ ;x ₃]−[y ₁ ′;x ₃′]∥²  (11)E _(rae3)=∥[y ₂ ;x ₄]−[y ₂ ′;x ₄′]∥²  (12)

where E_(rae1) denotes a Euclidean distance between an input layer x₁;x₂and a decoding (the decoding process is represented with dotted lines inFIG. 7) value [x₁′;x₂′] of an output layer [x₁;x₂], [x₁′;x₂′]=yW₁^((out)), and an initial value of W₁ ^((out)) is W^((xcomp)) ^(T) ;denotes a Euclidean distance between [y₁;x₃] of the input layer and adecoding (the decoding process is represented with dotted lines in FIG.7) value [y₁′;x₃′] of the output layer, [y₁′;x₃′]=y₂W₂ ^((out)), and aninitial value of W₂ ^((out)) is W^((prep)) ^(T) ; E_(rae3) denotes aEuclidean distance between an input layer [y₂;x₄] and a decoding (thedecoding process is represented with dotted lines in FIG. 7) value[y₂′;x₄′] of an output layer [y₂;x₄], [y₂′;x₄′]=y₃W₃ ^((out)), and aninitial value of W₃ ^((out)) is W^((prep)) ^(T) ; during actualapplications, steps 1) to 3) can be performed multiple times, each timey₁, y₂ and y₃ mapped by the hidden layer are decoded at the outputlayer, the updated w₁ ^((out)), W₂ ^((out)) and W₃ ^((out)) are alwaysused to correspondingly decode y₁, y₂ and y₃; in FIG. 7, whenW^((xcomp)), W^((prep)) and W^((pobj)) are trained, W₁ ^((out)), W₂^((out)) and W₃ ^((out)) will be used to correspondingly recursivelydecode y₁, y₂ and y₃, and thus the DNN shown in FIG. 7 can be regardedas a recursive self-decoder.

Step 4: Train the DNN, to extract first opinion information.

1) Train the DNN

Vectors c_(e)={c_(o),c_(t),c_(r)} corresponding to the evaluation termseed, the evaluation object seed and the evaluation relationship seeds_(e)={s_(o),s_(t),s_(r)} are connected at an input layer of an upperneural network shown in FIG. 8, a vector input by the input layer iscompressed at the hidden layer, to minimize a Euclidean distance betweena vector obtained after the output layer decodes and outputs thecompressed vector and the vector before compression.

2) Extract First Opinion Information.

As shown in FIG. 8, the vectors v_(o) and v_(t) corresponding to theevaluation term seed and the evaluation object seed obtained throughword vector learning shown in FIG. 6 and the vector v_(r) of theevaluation relationship seed obtained through the self-decoder shown inFIG. 7 are connected into a vector v_(e)=(v_(o),v_(t),v_(r)) at theinput layer of the upper DNN shown in FIG. 8. Moreover, the number ofnodes of the hidden layer of the upper DNN is less than that of theinput layer, as each node carries an operation of one dimension of thevector, a vector whose dimension is less than a dimension of v_(e) willbe obtained in the hidden layer by using such as “bottleneck” networkstructure, that is, semantic or syntactic features of the vector v_(e)input by the input layer are compressed at the hidden layer; at theoutput layer of the upper decoder of the DNN, a vector obtained by thehidden layer after the vector v_(e) is compressed is decoded and output,as the number of nodes of the output layer is the same as that of theinput layer, each node is used for carrying an operation of onedimension of the vector v_(e), the dimension of a decoding vector of thevector v_(e) obtained at the output layer is the same as that of thevector v_(e), and the upper DNN shown in FIG. 8 implements compressionand decoding functions, which may also be regarded as a self-decoder.

The structure of the self-decoder shown in FIG. 8 is described below.During implementation of the present disclosure, for the self-decodershown in FIG. 8, if one positive example opinion information is input atthe input layer, that is, the vector v_(e)=(v_(o),v_(t),v_(r))corresponding to s_(e)={s_(o),s_(t),s_(r)}, as, inv_(e)=(v_(o),v_(t),v_(r)), the closer the semantic or syntactic featuresbetween vectors are, the smaller the Euclidean distance between thevectors is (refer to step 3 for details), semantic or syntactic featuresof an opinion information positive example can be well decoded at theoutput layer (that is, a less decoding error value will be obtained).Alternatively, if a vector corresponding to one negative example opinioninformation is input at the input layer, the decoding error value of thevector output at the output layer will be greater than that of thevector corresponding to the positive example opinion information, thusleading to failure of decoding at the output layer. Accordingly, theprocess of extracting opinion information (i.e., evaluation phrases)from an evaluation text can be converted to a process of verifying anoutput vector whose decoding error value is less than a decoding errorvalue threshold at the output layer when a vectorv_(c)=(v_(co),v_(ct),v_(cr)) corresponding to a candidate evaluationterm, a candidate evaluation object, and a candidate evaluationrelationship is input at the input layer, that is, the evaluation term,the evaluation object, and the evaluation relationship corresponding tothe output vector whose decoding error value is less than a decodingerror value threshold is taken as first opinion information.

The information processing apparatus can filter the vectors output bythe output layer according to a decoding error value threshold ϑ, andoutput a candidate evaluation term, a candidate evaluation object, and acandidate evaluation relationship corresponding to a vector in theoutput vectors whose decoding error value is less than ϑ as firstopinion information.

During actual applications, the value of ϑ can be determined byintroducing an evaluation object negative example, for example, commonnouns, for example, “thing” and “one”, which are not evaluation objects,can be used as an evaluation object negative example GN, an evaluationobject positive example VO is acquired from the SentiWordNet, and thevalue of ϑ is determined according to Formulas (13) and (14):

$\begin{matrix}{E_{\vartheta} = {{\sum\limits_{t \in {{GN}\bigcap{PE}}}\;\left\lbrack {{{pp}(t)} - 0} \right\rbrack^{2}} + {\sum\limits_{o \in {{VO}\bigcap{PE}}}\left\lbrack {{{pp}(o)} - 1} \right\rbrack^{2}}}} & (13) \\{{{{pp}(t)} = {t\;{{f^{+}(t)}/t}\;{f(t)}}},{{PE} = \left\{ {c_{e}❘{{E_{r}\left( c_{e} \right)} < \vartheta}} \right\}}} & (14)\end{matrix}$

where PE denotes opinion information, the opinion information (includingevaluation terms, evaluation objects and evaluation relationships)corresponds to a vector in the vectors output by the output layer whosedecoding error value is less than ϑ, E_(r) ( ) is a decoding error valueof the output layer, tf (t) is the number of times the evaluation objectt occurs in PE, and tf⁺ (t) is the number of times the evaluation objectt is correctly identified as an evaluation object positive example;pp(t) is a ratio of the number of times the evaluation object t in PE isidentified as an evaluation object positive example to the number oftimes the evaluation object occurs in the evaluation text, E_(ϑ) ispositively correlated with E_(ϑ) represents precision of extractingopinion information from the evaluation text by the informationprocessing apparatus, the less E_(ϑ) is, the higher the precision ofextracting opinion information is, but the number of the extractedopinion information is less, the greater E_(ϑ) is, the lower theprecision of extracting opinion information is, and the number of theextracted opinion information is correspondingly more (i.e., theproportion of identifying evaluation object negative samples asevaluation object positive samples increases). Therefore, the value of ϑcan be adjusted according to actual requirements. For example, inScenario 1, when the requirement for the precision of opinioninformation output by the output layer is higher but no attention ispaid to the number of the opinion information output, ϑ can be set as ahigher value, for example, 0.1, and at this time, a vector, whosedecoding error value is less than 0.1, in the vectors obtained throughdecoding of the output layer corresponds to the first opinioninformation. In Scenario 2, when the requirement for the precision ofopinion information output by the output layer is lower than that inScenario 1 and it is necessary to output more opinion information thanScenario 1, ϑ can be set as a value greater than that in Scenario 1, forexample, 0.2, and at this time, a vector, whose decoding error value isless than 0.2, in the vectors obtained through decoding of the outputlayer corresponds to the first opinion information.

It can be seen that, as the output vectors of the output layer includean evaluation term, an evaluation object and an evaluation relationshipat the same time, and the information processing apparatus filters theoutput vectors according to a decoding error value at the output layerof the upper DNN, equivalent to jointly verifying a candidate evaluationterm, a candidate evaluation object, and a candidate evaluationrelationship, to obtain opinion information, which, compared with thatonly two of the evaluation term, the evaluation object and theevaluation relationship are verified in the related art, the precisionof extracting opinion information is higher.

It should be noted that, in step 4, as the information processingapparatus uses the recursive self-decoder shown in FIG. 8 to merge aword vector of each word in the evaluation relationship through asyntactic dependency path, a vector output by the information processingapparatus whose decoding error value is less than the threshold ϑ willinclude opinion information indicating that the evaluation relationshipand the evaluation relationship seed are structurally similar, which isequivalent to expanding the evaluation relationship seed, and avoids theproblem that sparse distribution of the evaluation relationship seedcausing the opinion information to be incomplete.

Step 5: Re-train the DNN, to extract second opinion information.

That is, a new evaluation object seed is acquired from the first opinioninformation to be merged into an evaluation object seed set TS, to usethe TS as a new evaluation object seed to re-train the DNN, uponcompletion of the training, vectors corresponding to candidateevaluation terms, candidate evaluation objects and candidate evaluationrelationships are connected into a vector v_(c)=(v_(co),v_(ct),v_(cr))at the inputlayer of the upper DNN shown in FIG. 8, which is compressedat the hidden layer and is decoded at the output layer, and candidateevaluation terms, candidate evaluation objects, and candidate evaluationrelationships corresponding to vectors whose decoding error values areless than a decoding error value threshold are determined as secondopinion information; detailed description is given below.

The evaluation objects tin the opinion information output in step 5 arearranged in descending order according to Formula (15):s(t)=pp(t)×log tf(t)  (15)

where pp(t)=tf⁺ (t)/tf (t) indicates the number of times t is identifiedas an evaluation object positive example, and tf (t) indicates thenumber of times t occurs in an evaluation text;

s(t) reflects the number of times t occurs in the evaluation text andthe number of times t is identified as an evaluation object positiveexample, when the number of times t occurs in the evaluation text andthe number of times t is identified as an evaluation object positiveexample are greater, the probability that t is an evaluation objectpositive example is greater, on this basis, M evaluation objects t withthe greatest s(t) are taken as evaluation object seeds to beincorporated into the evaluation object seed set TS in step 1, and theexpanded TS is used to re-train the upper DNN shown in FIG. 8. Aftercompletion of training, vectors corresponding to the candidateevaluation object, the candidate evaluation term, and the candidateevaluation relationship are connected into a vectorv_(c)=(v_(co),v_(ct),v^(cr)) at the input layer of the upper DNN shownin FIG. 8, which is compressed at the hidden layer and is decoded at theoutput layer, and candidate evaluation terms, candidate evaluationobjects, and candidate evaluation relationships corresponding to vectorswhose decoding error values are less than a decoding error valuethreshold are determined as second opinion information.

As the TS is expanded, during word vector learning, the defect of sparsedistribution of evaluation objects in the TS can be overcome, so thatthe opinion information extracted in step 5 is more accurate.

In order to verify the influences of step 5 on the precision ofextracting opinion information from the evaluation text, the inventorcarries out the following experiment:

opinion information is extracted from evaluation texts in an MP3 field,a Hotel field, a Mattress field, and a Phone field according to themethod described in the embodiment of the present invention by using theDNN technology, each time the opinion information is extracted, Mevaluation objects t with the greatest s(t) are added to the evaluationobject seed set TS to re-train the upper DNN shown in FIG. 8, and theopinion information is re-extracted from the evaluation texts in thefields;

the experiment is carried out by using a DP method and LRTBOOT in theprior art, every extraction precision is as shown by the verticalcoordinates in FIG. 9a to FIG. 9d , and the horizontal coordinatesindicate the ratio of M to the number of the opinion informationextracted. It can be seen that the precision of extracting opinioninformation according to the method described in the embodiment of thepresent invention implemented with the DNN technology is evidentlysuperior to the related art. Moreover, with the expansion of theevaluation object seed set TS, the precision of extracting opinioninformation according to the method described in the embodiment of thepresent invention implemented with the DNN technology will first riseand then fall, this is because more and more evaluation objects areadded to the evaluation object set TS along with the expansion of theevaluation object seed set TS. Therefore, during application, step 6 canbe performed only once, so as to avoid adding noise evaluation objectsto the set TS as seeds to lead to a problem that precision of extractingopinion information is reduced.

In order to verify the beneficial effects brought about by the variousembodiments of the present disclosure, the method described in thevarious embodiments of the present disclosure implemented with the DNNtechnology is compared with the AdjRule method, the DP method, theDP-HIT method, and the LRTBOOT method, and the following experiment iscarried out:

A precision rate (denoted by P), a recall rate (denoted by R), and an Fvalue are used to assess results of extracting evaluation terms andevaluation objects from evaluation texts with different methods, whichare defined as follows:

P=the number of identified positive example words/the number of all theidentified words

R=the number of identified positive example words/the number of positiveexample words in the evaluation textsF=2PR/(P+R)

As shown in Table 1, when the same test data is used, the proportion ofextracting positive example words in different fields according to thetechnical solution in the various embodiments of the present disclosureis superior to that according to the method in the related art,

TABLE 1 MP3 Hotel Mattress Phone P R F P R F P R F P R F AverageEvaluation object AdjRule 0.53 0.50 0.51 0.53 0.54 0.55 0.50 0.60 0.550.52 0.51 0.51 0.53 DP 0.63 0.52 0.57 0.61 0.56 0.58 0.55 0.60 0.57 0.600.53 0.56 0.57 DP-HITS 0.64 0.61 0.62 0.63 0.64 0.63 0.55 0.67 0.60 0.620.64 0.63 0.62 LRTBOOT 0.60 0.72 0.65 0.57 0.74 0.64 0.55 0.78 0.65 0.570.76 0.65 0.65 OCDNN 0.71 0.64 0.67 0.72 0.66 0.69 0.63 0.69 0.66 0.690.68 0.68 0.68 Evaluation term AdjRule 0.48 0.60 0.53 0.50 0.61 0.550.51 0.68 0.58 0.48 0.61 0.54 0.55 DP 0.58 0.58 0.58 0.58 0.60 0.59 0.540.68 0.60 0.55 0.59 0.57 0.59 LRTBOOT 0.52 0.65 0.58 0.54 0.69 0.61 0.510.73 0.60 0.50 0.68 0.58 0.59 OCDNN 0.61 0.59 0.60 0.65 0.61 0.63 0.590.70 0.64 0.61 0.59 0.60 0.62

Table 2 and Table 3 give the proportion of extracting opinioninformation (including evaluation terms, evaluation objects andevaluation relationships) according to the technical solution in thevarious embodiments of the present disclosure. During application, theprecision of extracting opinion information from an evaluation text ismore convincing than the precision of simply identifying positiveexample evaluation terms and positive example evaluation objects, and itcan be seen from Table 2 and Table 3 that the P, R, and F parametersachieved in the technical solution in the various embodiments of thepresent disclosure are superior to the P, R, and F parameters achievedin the related art. D1-D2 represent evaluation text test libraries indifferent fields.

TABLE 2 D1 D2 D3 P R F P R F P R F AdjRule 0.51 0.66 0.58 0.53 0.63 0.580.5 0.61 0.6 DP 0.66 0.63 0.64 0.68 0.6 0.64 0.69 0.62 0.7 LRTBOOT 0.530.7 0.6 0.57 0.72 0.64 0.55 0.69 0.6 OCDNN 0.76 0.66 0.71 0.74 0.67 0.70.77 0.67 0.7 D4 D5 P R F P R F average AdjRule 0.48 0.6 0.53 0.5 0.610.55 0.6 DP 0.66 0.57 0.61 0.67 0.6 0.63 0.6 LRTBOOT 0.52 0.7 0.6 0.650.68 0.61 0.6 OCDNN 0.7 0.65 0.67 0.77 0.66 0.71 0.7

TABLE 3 MP3 Hotel P R F P R F average AdjRule 0.49 0.55 0.52 0.45 0.530.49 0.50 DP 0.63 0.51 0.56 0.59 0.50 0.54 0.55 LRTBOOT 0.54 0.63 0.580.50 0.60 0.55 0.56 OCDNN 0.73 0.60 0.66 0.70 0.59 0.64 0.65

A person of ordinary skill in the art may understand that all or some ofthe steps of the foregoing various method embodiments may be implementedby a program instructing relevant hardware. The program may be stored ina computer readable storage medium, and during execution of the program,the execution includes the steps of the foregoing various methodembodiments. The storage medium includes any medium that can storeprogram codes, such as a mobile storage device, a random access memory(RAM), a read-only memory (ROM), a magnetic disk, or an optical disc.

When the integrated units of the present disclosure are implemented in aform of a software functional module and sold or used as an independentproduct, the integrated units may be stored in a computer-readablestorage medium. Based on such an understanding, the technical solutionsof the various embodiments of the present disclosure, or the partcontributing to the related art, may be embodied in the form of asoftware product. The computer software product is stored in a storagemedium and includes several instructions for instructing a computerdevice (which may be a personal computer, a server, or a network device)to perform all or a part of the steps of the methods in the variousembodiments of the present disclosure. The foregoing storage mediumincludes any medium that can store program codes, such as a mobilestorage device, a RAM, a ROM, a magnetic disk, or an optical disc.

The above descriptions are example implementations of the presentdisclosure, but the protection scope of the present disclosure is notlimited thereto. Variations or replacements that can be easily thoughtof by any person skilled in the art within the technical scope disclosedin the present disclosure should be included in the protection scope ofthe present disclosure. Therefore, the protection scope of the presentdisclosure should be subject to the protection scope of the claims.

What is claimed is:
 1. An information processing method, the methodcomprising: training, by a computing device, a Deep Neural Network (DNN)by using an evaluation object seed, an evaluation term seed and anevaluation relationship seed, the DNN comprising a first input layer, afirst hidden layer and a first output layer, a number of nodes of thefirst input layer corresponding to that of the first output layer, andthe number of nodes of the first input layer being greater than that ofthe first hidden layer; at the first input layer, connecting, by thecomputing device, vectors corresponding to a candidate evaluationobject, a candidate evaluation term and a candidate evaluationrelationship to obtain a first input vector, at the first hidden layer,compressing, by the computing device, the first input vector to obtain afirst middle vector, and at the first output layer, decoding the firstmiddle vector to obtain a first output vector; determining, by thecomputing device, whether a decoding error value of the first outputvector is less than a decoding error value threshold, if yes,determining the candidate evaluation object, the candidate evaluationterm and the candidate evaluation relationship corresponding to thefirst output vector as first opinion information; wherein at the firstinput layer of the DNN, before connecting vectors corresponding to thecandidate evaluation object, the candidate evaluation term and thecandidate evaluation relationship to obtain the first input vector, themethod further includes: mapping, by the computing device, the candidateevaluation object and the candidate evaluation term into correspondingvectors by using the DNN; and at a second hidden layer to an nth hiddenlayer of the DNN, recursively mapping, by the computing device, objectscomprised in a syntactic dependency path of the candidate evaluationrelationship into vectors, wherein a Euclidean distance between any twovectors in the vectors obtained through mapping is positively correlatedwith a semantic or syntactic likelihood ratio test (LRT) index of theany two vectors, n is a positive integer, and n corresponds to thenumber of the objects comprised in the syntactic dependency path of thecandidate evaluation relationship, wherein the training the DNN by usingthe evaluation object seed, the evaluation term seed and the evaluationrelationship seed comprises: at the first input layer, connecting, bythe computing device, vectors corresponding to the evaluation objectseed, the evaluation term seed and the evaluation relationship seed toobtain a second input vector; at the first hidden layer, compressing, bythe computing device, the second input vector to obtain a second middlevector; and updating, by the computing device, a parameter set of theDNN until a Euclidean distance between a second output vector and thesecond input vector is minimum, the second output vector being a vectorobtained by decoding the second middle vector at the first output layer,and wherein the compressing and the decoding, by the computing device,are performed by implementing a recursive self-decoder which, each timethe parameter set of the DNN is updated, recursively decode the firstmiddle vector to obtain the first output vector.
 2. The method accordingto claim 1, wherein the method further comprises: sorting, by thecomputing device, evaluation objects in the first opinion informationaccording to the following dimensions in descending order: the number oftimes the evaluation objects in the first opinion information occur inan evaluation text, and the number of times the evaluation objects inthe first opinion information are identified as evaluation objectpositive examples; selecting, by the computing device, M evaluationobjects, in descending order, from the evaluation objects sorted indescending order, and determining the selected M evaluation objects as asubset of the evaluation object seed, M being an integer greater than 1;and training, by the computing device, the DNN by using the evaluationobject seed, the evaluation term seed and the evaluation relationshipseed after updating the parameter set of the DNN.
 3. The methodaccording to claim 2, wherein, after the training the DNN by using theevaluation object seed, the evaluation term seed and the evaluationrelationship seed after updating the parameter set of the DNN, themethod further comprises: at the first input layer of the DNN afterupdating the parameter set of the DNN, connecting, by the computingdevice, vectors corresponding to the candidate evaluation object, thecandidate evaluation term and the candidate evaluation relationship toobtain a third input vector, at the first hidden layer of the DNN afterupdating the parameter set of the DNN, compressing, by the computingdevice, the third input vector to obtain a third middle vector, and atthe first output layer of the DNN after updating the parameter set ofthe DNN, decoding the third middle vector to obtain a third outputvector; and determining, by the computing device, whether a decodingerror value of the third output vector is less than a decoding errorvalue threshold, if yes, determining the candidate evaluation object,the candidate evaluation term and the candidate evaluation relationshipcorresponding to the third output vector as second opinion information.4. The method according to claim 1, wherein, before determining whetherthe decoding error value of the first output vector is less than thedecoding error value threshold, the method further comprises:determining, by the computing device, the decoding error value thresholdaccording to the following dimensions: target precision of opinioninformation extracted from an evaluation text, and target quantity ofthe opinion information extracted from the evaluation text, wherein thedecoding error value threshold is negatively correlated with the targetprecision, and is positively correlated with the target quantity.
 5. Aninformation processing apparatus, comprising: a processor for executinginstructions stored in a non-transitory machine readable storage mediumto: train a Deep Neural Network (DNN) by using an evaluation objectseed, an evaluation term seed and an evaluation relationship seed, theDNN comprising a first input layer, a first hidden layer and a firstoutput layer, a number of nodes of the first input layer correspondingto that of the first output layer, and the number of nodes of the firstinput layer being greater than that of the first hidden layer; at thefirst input layer, connect vectors corresponding to a candidateevaluation object, a candidate evaluation term and a candidateevaluation relationship to obtain a first input vector; at the firsthidden layer, compress the first input vector to obtain a first middlevector; at the first output layer, decode the first middle vector toobtain a first output vector; determine whether a decoding error valuethe first output vector is less than a decoding error value threshold,if yes, determine the candidate evaluation object, the candidateevaluation term and the candidate evaluation relationship correspondingto the first output vector as first opinion information; wherein theinstructions stored in the non-transitory machine readable storagemedium further cause the processor to: map the candidate evaluationobject and the candidate evaluation term into corresponding vectors byusing the DNN; and at a second hidden layer to an nth hidden layer ofthe DNN, recursively map objects comprised in a syntactic dependencypath of the candidate evaluation relationship into vectors, wherein aEuclidean distance between any two vectors in the vectors obtainedthrough mapping is positively correlated with a semantic or syntacticlikelihood ratio test (LRT) index of the any two vectors, n is apositive integer, and n corresponds to the number of the objectscomprised in the syntactic dependency path of the candidate evaluationrelationship, wherein the instructions stored in the non-transitorymachine readable storage medium further cause the processor to: at thefirst input layer, connect vectors corresponding to the evaluationobject seed, the evaluation term seed and the evaluation relationshipseed to obtain a second input vector; at the first hidden layer,compress the second input vector to obtain a second middle vector; atthe first output layer, decode the second middle vector to obtain asecond output vector; and update a parameter set of the DNN until aEuclidean distance between the second output vector and the second inputvector is minimum, and wherein compressing of the first input vector anddecoding the first middle vector are performed by implementing arecursive self-decoder which, each time the parameter set of the DNN isupdated, recursively decode the first middle vector to obtain the firstoutput vector.
 6. The apparatus according to claim 5, wherein theinstructions stored in the non-transitory machine readable storagemedium further cause the processor to: sort evaluation objects in thefirst opinion information according to the following dimensions indescending order: the number of times the evaluation objects in thefirst opinion information occur in an evaluation text; and the number oftimes the evaluation objects in the first opinion information areidentified as evaluation object positive examples; select M evaluationobjects, in descending order, from the evaluation objects sorted indescending order, and determine the selected M evaluation objects as asubset of the evaluation object seed, M being an integer greater than 1;and train the DNN by using the evaluation object seed, the evaluationterm seed and the evaluation relationship seed after updating theparameter set of the DNN.
 7. The apparatus according to claim 6, whereinthe instructions stored in the non-transitory machine readable storagemedium further cause the processor to: at the first input layer of theDNN after updating the parameter set of the DNN, connect vectorscorresponding to the candidate evaluation object, the candidateevaluation term and the candidate evaluation relationship to obtain athird input vector; at the first hidden layer of the DNN after updatingthe parameter set of the DNN, compress the third input vector to obtaina third middle vector; at the first output layer of the DNN afterupdating the parameter set of the DNN, decode the third middle vector toobtain a third output vector; and determine whether a decoding errorvalue of the third output vector is less than a decoding error valuethreshold, if yes, determine the candidate evaluation object, thecandidate evaluation term and the candidate evaluation relationshipcorresponding to the third output vector as second opinion information.8. The apparatus according to claim 5, wherein the instructions storedin the non-transitory machine readable storage medium further cause theprocessor to: before determining whether the decoding error value of thefirst output vector is less than the decoding error value threshold,determine the decoding error value threshold according to the followingdimensions: target precision of opinion information extracted from anevaluation text; and target quantity of the opinion informationextracted from the evaluation text, wherein the decoding error valuethreshold is negatively correlated with the target precision, and ispositively correlated with the target quantity.
 9. An informationprocessing method, the method comprising: training, by a computingdevice, a Deep Neural Network (DNN) by using an evaluation object seed,an evaluation term seed and an evaluation relationship seed, the DNNcomprising a first input layer, a first hidden layer and a first outputlayer, a number of nodes of the first input layer corresponding to thatof the first output layer, and the number of nodes of the first inputlayer being greater than that of the first hidden layer; at the firstinput layer, connecting, by the computing device, vectors correspondingto a candidate evaluation object, a candidate evaluation term and acandidate evaluation relationship to obtain a first input vector, at thefirst hidden layer, compressing, by the computing device, the firstinput vector to obtain a first middle vector, and at the first outputlayer, decoding the first middle vector to obtain a first output vector;and determining, by the computing device, whether a decoding error valueof the first output vector is less than a decoding error valuethreshold, if yes, determining the candidate evaluation object, thecandidate evaluation term and the candidate evaluation relationshipcorresponding to the first output vector as first opinion information;wherein at the first input layer of the DNN, before connecting vectorscorresponding to the candidate evaluation object, the candidateevaluation term and the candidate evaluation relationship to obtain thefirst input vector, the method further includes: extracting, by thecomputing device, nouns from an evaluation text; determining, by thecomputing device, a likelihood ratio test (LRT) index between an initialevaluation object seed and the nouns extracted from the evaluation text;and determining, by the computing device, nouns in the extracted nounsas a subset of the evaluation object seed, wherein the LRT index betweenthe determined nouns and the initial evaluation object seed is greaterthan an LRT index threshold wherein the training the DNN by using theevaluation object seed, the evaluation term seed and the evaluationrelationship seed comprises: at the first input layer, connecting, bythe computing device, vectors corresponding to the evaluation objectseed, the evaluation term seed and the evaluation relationship seed toobtain a second input vector; at the first hidden layer, compressing, bythe computing device, the second input vector to obtain a second middlevector; and updating, by the computing device, a parameter set of theDNN until a Euclidean distance between a second output vector and thesecond input vector is minimum, the second output vector being a vectorobtained by decoding the second middle vector at the first output layer,and wherein the compressing and the decoding, by the computing device,are performed by implementing a recursive self-decoder which, each timethe parameter set of the DNN is updated, recursively decode the firstmiddle vector to obtain the first output vector.